Detection of volatile organic compounds

This application claims priority to U.S. Provisional Patent Application No. 63/430,304 entitled DETECTION AND ANALYSIS OF AIR TOXICS filed Dec. 5, 2022 which is incorporated herein by reference for all purposes.

Volatile Organic Compounds (VOCs) are chemicals that are of interest to be detected because they are potential contaminants in air and may be both acutely and chronically hazardous, particularly if emitted in high enough amounts. Health hazards of VOCs include serious respiratory issues, neurological issues, reproductive and developmental issues, and cancer. They may also lead to secondary formation of other hazardous air pollutants such as fine particulate matter (PM2.5) and ozone (O3). The Environmental Protection Agency (EPA) maintains a list of 188 hazardous air pollutants that it seeks to reduce emissions of under the Clean Air Act, and most of these pollutants fall into the category of VOCs. Some examples of these hazardous VOCs include BTEX chemicals (benzene, ethylbenzene, toluene, and xylene). Although emission of VOCs may be legal, the emission is also typically regulated. While regulatory authorities may grant permits to businesses to emit certain hazardous VOCs, the impact of these emissions is often not well understood, including the locations impacted and the concentrations at those locations. Additionally, in complex urban and industrial environments, there are typically many sources of VOC emissions that are not permitted and/or not known to be a concern by the regulators. Determining the geographic distribution of VOC pollution in a particular region typically requires deployment of expensive instrumentation, operated by experts and can only be done on very small scales. Thus, capturing and analyzing VOC data for a large region may be time consuming, inefficient, and costly.

The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.

Embodiments of detection of volatile organic compounds (VOCs) are described herein. The mobile sensor data is received. In various embodiments, the mobile sensor data comprises data streams that are collected by sets of sensors that are located on mobile sensor platforms that are mounted on one or more vehicle(s), such as automobile(s) or drone(s). In various embodiments, each set of sensors comprises a VOC sensor that is configured to detect the presence of at least one type of VOC. In some embodiments, the VOC sensor that is included in each set of sensors is a total VOCs (TVOC) sensor, which detects the presence of any one of multiple types of VOCs. In various embodiments, each set of sensors comprises at least one non-VOC sensor that is configured to detect the presence of a pollutant other than a VOC. Examples of non-VOC pollutants include carbon monoxide, carbon dioxide, nitric oxide, methane, and fine particulate matter. In various embodiments, the mobile sensor data comprises multiple independent sensor data streams that are generated by respective sensors of different types and/or across different vehicles. In various embodiments, a sensor data stream comprises a series of sensor data points (e.g., concentration amounts) with corresponding time and location information associated with each data point. VOC peaks are identified in the received VOC sensor data. Non-VOC peaks are identified in the received non-VOC sensor data. In various embodiments, a VOC peak is determined as a local peak within the data points in its temporal neighborhood. Each VOC peak is associated with at least corresponding time information and location information. In various embodiments, a non-VOC peak is similarly determined and is also therefore associated with at least corresponding time information and location information. The VOC peaks are correlated with the non-VOC peaks. A source type classification associated with the VOC peaks is determined based at least in part on the correlation between the VOC peaks and the non-VOC peaks. In various embodiments, a source type classification of a VOC peak describes the possible type of source or event that led to the VOC peak. The source type classification of a VOC peak could ultimately trigger/inform the type of follow up research (e.g., using more sensitive and specific measurement instruments) that may be performed at the location associated with the VOC peak, for example.

1 FIG. 100 102 102 102 150 100 103 103 103 102 102 102 103 102 102 102 103 150 102 102 102 102 102 102 103 150 108 depicts an embodiment of a system for collecting and processing environmental data. Systemincludes multiple mobile sensor platformsA,B,C and server. In some embodiments, systemmay also include one or more stationary sensor platforms, of which one is shown. Stationary sensor platformmay be used to collect environmental data at a fixed location. The environmental data collected by stationary sensor platformmay supplement the data collected by mobile sensor platformsA,B andC. Thus, stationary sensor platformmay have sensors that are the same as or analogous to the sensors for mobile sensor platformsA,B andC. In other embodiments, stationary sensor platformmay be omitted. Although a single server (server) is shown, multiple servers may be used. The multiple servers may be in different locations. Although three mobile sensor platformsA,B andC are shown, another number are typically present. Mobile sensor platformsA,B andC and stationary sensor platform(s)may communicate with servervia a data network. The communication may take place wirelessly.

102 102 102 102 102 102 102 106 110 120 130 102 110 120 130 110 120 130 100 110 120 130 110 112 114 116 120 122 124 126 130 132 134 110 120 130 104 110 120 130 110 120 130 110 110 2 3 2 2 4 2 6 2 3 2 3 Mobile sensor platformsA,B, andC may be mounted on respective vehicles, such as automobiles or drones. In some embodiments, mobile sensor platformsA,B, andC are desired to stay in proximity to the ground to be better able to sense conditions analogous to what a human would experience. Mobile sensor platformA includes a bus, sensors,and. Although three sensors are shown, another number may be present on mobile sensor platformA. In addition, a different configuration of components may be used with sensors,and. Each sensor,andis used to sense environmental quality and may be of primary interest to a user of system. For example, sensors,andmay be gas sensors, volatile organic compound (VOC) sensors, particulate matter sensors, radiation sensors, noise sensors, light sensors, temperature sensors, or other analogous sensors that capture variations in the environment. For example, sensors(which in turns includes sensors,, and),(which in turns includes sensors,, and), and(which in turns includes sensorsand) may be used to sense one or more of NO, CO, NO, O, SO, CO, VOCs, CH, CH, particulate matter, black carbon, noise, light, temperature, radiation, other environmental components (e.g. other ambient gas(es)), and/or other parameters. In various embodiments, at least one of sensors,, andof mobile sensor platformincludes a total VOC (TVOC) sensor, which detects the presence of any of several types of VOCs (instead of a single type of VOC). One advantage of using a TVOC sensor is that one TVOC sensor is more cost effective and space efficient than using several VOC type-specific sensors or using a single sensitive instrument that could detect the respective concentration of each of several types of VOCs, such as a mass spectrometer. In various embodiments, sensors,, andare sensors that operate independently of each other and therefore, generate three separate sensor data streams (e.g., each sensor data stream is its own time series) and each such sensor can be replaced without affecting the other two sensors on the same mobile sensor platform. In some embodiments, sensor,and/ormay be a multi-modality sensor. A multi-modality gas sensor senses multiple gases or compounds. For example, if sensoris a multi-modality NO/Osensor, sensormight sense both NOand Otogether.

1 FIG. 110 120 130 110 120 130 110 120 130 110 120 130 110 120 130 102 110 120 130 Although not shown in, other sensors co-located with sensors,andmay be used to sense characteristics of the surrounding environment including, in some instances, other gases, meteorological data, and/or particulate matter. Such additional sensors are exposed to the same environment as sensors,and. In some embodiments, such additional sensors are in close proximity to sensors,and, for example within ten millimeters or less. In some embodiments, the additional sensors may be further from sensors,andif the additional sensors sample the same packet of air inside of a closed system, such as a system of closed tubes. In some embodiments, temperature and/or pressure are sensed by these additional sensors. These additional co-located sensors may be used to calibrate sensors,and/or. Although not shown, sensor platformA may also include a manifold for drawing in air and transporting air to sensors,andfor testing.

110 120 130 106 110 120 130 102 140 150 110 120 130 150 110 120 130 110 120 130 110 120 130 110 120 130 Sensors,andprovide sensor data over bus, or via another mechanism. In some embodiments, data from sensors,andincorporates time. This time may be provided by a master clock (not shown) and may take the form of a timestamp. The master clock may reside on sensor platformA, may be part of processing unit, or may be provided from server. As a result, sensors,andmay provide timestamped sensor data to server. In other embodiments, the time associated with the sensor data may be provided in another manner. Because sensors,andgenerally capture data at a particular frequency, sensor data is discussed as being associated with a particular time interval (e.g., the period associated with the frequency), though the sensor data may be timestamped with a particular value. For example, sensors,and/ormay capture sensor data every second, every two seconds, every ten seconds, or every thirty seconds. The time interval may be the same for all sensors,andor may differ for different sensors,and. In some embodiments, the time interval for a sensor data point is centered on the timestamp. For example, if the time interval is one second and a timestamp is t1, then the time interval may be from t1−0.5 seconds to t1+0.5 seconds. However, other mechanisms for defining the time interval may be used.

102 145 145 100 145 145 130 Sensor platformA also includes a position unitthat provides position data. In some embodiments, position unitis a global positioning satellite (GPS) unit. Consequently, systemis described in the context of a GPS unit. The position data may be time-stamped in a manner analogous to sensor data. Because position data is to be associated with sensor data, the position data may also be considered associated with time intervals, as described above. However, in some embodiments, position data (e.g., GPS data) may be captured more or less frequently than sensor data. For example, GPS unitmay capture position data every second, while sensormay capture data every thirty seconds. Thus, multiple data points for the position data may be associated with a single thirty second time interval. The position data may be processed as described below.

140 104 104 150 Optional processing unitmay perform some processing and functions for data from sensor platform, may simply pass data from sensor platformto serveror may be omitted.

102 102 102 102 102 102 102 102 102 Mobile sensors platformsB andC are analogous to mobile sensor platformA. In some embodiments, mobile sensor platformsB andC have the same components as mobile sensor platformA. However, in other embodiments, the components may differ. However, mobile sensor platformsA,B andC function in an analogous manner.

150 152 154 156 158 154 154 156 156 154 154 156 Serverincludes sensor data database, processor(s), memoryand position data database. Processor(s)may include multiple cores. Processor(s)may include one or more central processing units (CPUs), one or more graphical processing units (GPUs) and/or one or more other processing units. Memorycan include a first primary storage, typically a random-access memory (RAM), and a second primary storage area, typically a non-volatile storage such as solid-state drive (SSD) or hard disk drive (HDD). Memorystores programming instructions and data for processes operating on processor(s). Primary storage typically includes basic operating instructions, program code, data and objects used by processor(s)to perform their functions. Primary storage devices (e.g., memory) may include any suitable computer-readable storage media, described below, depending on whether, for example, data access needs to be bi-directional or uni-directional.

152 102 102 102 102 102 102 152 158 102 102 102 152 158 150 150 110 120 130 Sensor data databaseincludes data received from mobile sensor platformsA,B and/orC. After capture by mobile sensor platformA,B and/orC, sensor data stored in sensor data databasemay be operated on by various analytics, as described below. Position data databasestores position data received from mobile sensor platformsA,B and/orC. In some embodiments, sensor data databasestores position data as well as sensor data. In such embodiments, position data databasemay be omitted. Servermay include other databases and/or store and utilize other data. For example, servermay include calibration data (not shown) used in calibrating sensors,and.

100 102 102 102 150 150 150 102 102 102 102 102 102 150 100 102 102 102 Systemmay be used to capture, analyze, and provide information regarding hyper-local environmental data. Mobile sensor platformsA,B, andC may be used to traverse routes and provide sensor and position data to server. Servermay process the sensor data and position data. Servermay also assign the sensor data to map features corresponding to the locations of mobile sensor platformsA,B, andC within the same time interval as the sensor data was captured. As discussed above, these map features may be hyper-local (e.g., one hundred meter or less road segments or thirty meter or less road segments). Thus, mobile sensor platformsA,B, andC may provide sensor data that can capture variations on this hyper-local distance scale. Servermay provide the environmental data, a score, confidence score and/or other assessment of the environmental data to a user. Thus, using systemhyper-local environmental data may be obtained using a relatively sparse network of mobile sensor platformsA,B, andC, associated with hyper-local map features and processed for improved understanding of users.

2 FIG. 200 102 102 102 150 200 100 200 depicts an exemplary embodiment of methodfor capturing environmental data using mobile sensor platforms, such as mobile sensor platformsA,B and/orC and server. Methodis described in the context of system, but may be performed using other systems. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order.

202 202 202 2 3 2 2 4 2 6 Mobile sensor platforms traverse routes in a geographic area, at. While traversing the routes, the mobile sensor platforms collect not only sensor data, but also position data. For example, a mobile sensor platform may sense one or more of NO, CO, NO, O, SO, CO, CH, CH, VOCs, particulate matter in one or more size ranges, other compounds, radiation, noise, light and other environmental data at various times during traversal of the route. Other environmental characteristics, including but not limited to temperature, pressure, and/or humidity may also be sensed at. In addition, the time corresponding to the environmental data is also captured. The time may be in the form of a timestamp for the sensor data (sensor timestamp), which may correspond to a particular time interval. Different sensors on the mobile sensor platform may capture the environmental data at different times and/or at different frequencies. Also at, the mobile sensor platforms capture position data, for example via a GPS unit. The position data may include location (as indicated by a GPS unit), velocity and/or other information related to the geographic location of the mobile sensor platform. In some embodiments, position data from other sources, such as acceleration, may be captured from the vehicle or another source. The position data may include a timestamp (position timestamp) or other indicator of the time at which the position data is captured.

204 202 The mobile sensor platforms provide the position and sensor data to a server, at. In some embodiments, mobile sensor platforms provide this data substantially in real-time, as the mobile sensor platforms traverse their routes at. Thus, the position and sensor data may be transmitted wirelessly to the server. In some embodiments, some or all of the position and/or sensor data is stored at the mobile sensor platform and provided to the server at a later time. For example, the data may be transferred to the server when the mobile sensor platform returns to its base. In some embodiments, the mobile sensor platform may process the sensor data and/or position data prior to sending the sensor and/or position data to the server. In other embodiments, the mobile sensor platform provides little or no processing. The sensor data and position data may be sent at the same time or may be sent separately.

206 202 204 206 206 206 206 206 202 204 206 At, the route traversal and data collecting ofand data sending ofare repeated. Thus, the mobile sensor platforms may traverse the same or different routes at. In either case, multiple passes of the same geographic locations, and thus multiple passes of the same corresponding map features, are made at. In some embodiments, the repetition atmay be periodic (e.g., approximately every week, month or other time period). In some embodiments, the repetition atmay be performed based on other timing. In some cases, the same mobile sensor platform is sent on the same route and/or collects data for the same map features. In some embodiments, different mobile sensor platforms collect data that may be used for the same routes and/or map features. Also at, stepsandmay be performed multiple times. Thus, at, data for a particular region may be aggregated over time.

3 3 FIGS.A-C 3 FIG.A 3 FIG.A 200 300 300 300 310 312 314 320 322 324 320 322 324 320 322 300 310 312 314 322 For example,illustrate a particular geographic area and the routes that may be traversed using method. A mapcorresponding to the geographic area is shown in. Mapmay be an open-source map or generated by another mapping tool. Mapincludes streets(oriented vertically on the page) and(oriented horizontally on the page); larger street/highway, structuresandand open area. For simplicity, only one of each structureandis labeled. Open areamay correspond to a park, vacant lot or analogous item. As can be seen in, the density and size of structuresandvary across map. Similarly, the density and size of streets,andalso varies. In addition, structuresare more clearly separated by open regions, which may correspond to a yard or analogous area.

3 FIG.B 3 FIG.B 300 330 102 202 102 330 330 312 314 300 330 102 330 202 110 120 130 202 145 330 102 330 102 102 150 204 102 330 102 102 150 202 204 200 illustrates mapas well as routethat may be traversed by a mobile sensor platform, such as mobile sensor platformA. At, mobile sensor platformA may traverse route. As can be seen in, the routeincludes a portion of each streetandin map. Some portions of some streets are traversed multiple times for the same route. In some embodiments, this is still considered a single pass of these streets. As mobile sensor platformA traverses routeat, sensor data is captured by sensors,and. Also at, position data is captured by GPS unitthroughout route. In some embodiments, the vehicle carrying mobile sensor platformA travels sufficiently slowly while traversing routethat sensor data and position data can be accurately captured for particular position(s). In some embodiments, mobile sensor platformA travels at a velocity that allows for multiple sensor data points for each map feature. Mobile sensor platformA also sends position and sensor data to serverat. This may be done while mobile sensor platformA traverses routeor at a later time. Other mobile sensor platformsB and/orC may also traverse the same or different routes and send data to serveratand. Thus, multiple mobile sensor platforms may be used in method.

206 102 102 102 102 102 102 330 102 102 102 300 332 206 102 102 102 332 206 202 102 102 102 332 102 102 102 102 102 102 150 206 204 330 332 3 FIG.C At, mobile sensor platformA and/or other mobile sensor platform(s)B andC repeat the route traversal, data collection and sending of the position and sensor data. In some cases, mobile sensor platform(s)A,B and/orC follow routeagain. In some cases, mobile sensor platform(s)A,B and/orC traverses a different route. For example,depicts mapwith another route. As part of, mobile sensor platform(s)A,B and/orC may traverse route, collecting position and sensor data at(repeating). In some embodiments, the vehicle carrying mobile sensor platform(s)A,B and/orC travels sufficiently slowly while traversing routethat sensor data and position data can be accurately captured for particular position(s). In some embodiments, mobile sensor platform(s)A,B and/orC travels at a velocity that allows for multiple sensor data points for each map feature (described below). Mobile sensor platform(s)A,B and/orC send sensor and position data to serverat(repeating) during or after traversing routeand/or route.

200 150 200 200 Thus, using method, sensor and position data may be captured for regions of a map. The sensor data and position data may be provided to serveror other component for processing, aggregation, and analysis. Sensor data and position data are sensed sufficiently frequently using methodthat variations in environmental quality on the hyper-local scales may be reflected in the sensor data. Methodmay be performed using a relatively small number of mobile sensor platforms. Consequently, efficiency of data gathering may be improved while maintaining sufficient sensitivity in both sensor and position data.

150 104 The sensor data captured and provided to serveris further processed. For example, data from multiple runs of mobile sensor platforms, such as mobile sensor platform, and stationary sensor data may be aggregated. In particular, data for locations (e.g., each road segment) captured on multiple runs at different times may be aggregated, additional processing performed, and statistical values such as the mean, average, and percentiles, calculated. Other processing may include background correction. For example, background values for particulate matter may be measured on days with rain or a specified portion of the lowest part of the distribution (e.g., the lowest ten percent or twenty percent) may be identified as the background. This background may then be subtracted from the distribution. Other techniques for accounting for the background may be used in other embodiments. In some embodiments, the background may be determined in different ways for different geographic regions (e.g., based on neighborhoods, cities or counties). In some embodiments, the background may be accounted for at only some locations.

200 100 200 100 102 100 Thus, using methodand system, environmental data may be sensed at hyperlocal scales and analyzed. In various embodiments, VOC sensor data as well as non-VOC sensor data are collected at hyperlocal scales and analyzed using methodand system. In some embodiments, each mobile sensor platform (e.g.,A of system) includes at least one TVOC sensor and a set of non-VOC sensors (sensors that are configured to respectively detect different types of pollutants other than VOCs). Different specific types of VOC pollutants (e.g., benzene, ethylbenzene, toluene, or xylene) may be associated with different types of sources. However, while the TVOC sensor does not indicate which specific VOC type pollutants are detected but only the collective amount of VOCs detected, it is advantageous to equip a mobile sensor platform with TVOCs because the cost of a single TVOC is less than the cumulative costs of various VOC type-specific sensors and also less than a single sensitive instrument that can detect the respective amounts of various specific VOC type pollutants. TVOC sensors are also beneficial in that a single TVOC sensor would occupy less volume/space per each mobile sensor platform than the alternative of multiple VOC type specific sensors or a single sensitive instrument that can detect the respective amounts of various specific VOC type pollutants. To analyze the VOC sensor data streams that are collected from TVOC sensors, peaks are first identified within the VOC sensor data streams and the non-VOC sensor data streams. Then, the VOC peaks are correlated with the non-VOC peaks to classify the source types of the VOC peaks, as will be described in further detail below. As such, the non-VOC sensor data can be leveraged to infer the source type classification of each VOC peak even if the measured TVOC data does not identify the specific VOC pollutants that make up the measurements.

4 FIG. 1 FIG. 400 400 100 400 102 102 102 150 400 depicts an exemplary embodiment of methodfor the detection of volatile organic compounds. Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel).

402 102 102 102 100 200 2 3 2 2 4 2 6 At, mobile sensor data is received, wherein the mobile sensor data includes volatile organic compounds sensor data and non-VOC sensor data. In various embodiments, the mobile sensor data is received from sensors that are part of mobile sensor platforms (e.g., mobile sensors platformsA,B, andC of system). As described above in method, mobile sensor platforms repeatedly traverse routes within a geographic area, while collecting sensor data that corresponds to time and location data. The mobile sensor platforms each includes a set of sensors, which comprises a sensor that detects VOC amounts (e.g., a TVOC sensor) and one or more sensors that detect non-VOC pollutants (e.g., NO, CO, NO, O, SO, CO, CH, and/or CH,). In some embodiments, the sensor that detects VOCs is independent from the sensor(s) that detect non-VOC data and therefore, the VOC sensor data and the non-VOC sensor data are independent data streams.

402 402 402 402 402 Sensor data are accrued while the mobile sensor platforms are in motion, at step. Thus, VOCs and other constituents of the air are sampled. At step, at least some of the VOCs and other measurements are made while the vehicle is in motion. In some embodiments, total VOCs are measured at step. In some embodiments, data are sampled at a frequency of at least 0.5 Hz and not more than 4 Hz. In some embodiments, data are sampled at 1 Hz. Other sampling frequencies are possible. In some embodiments, additional and/or other substances may also be detected at step. Further, data are passively sampled at step. In some embodiments, therefore, the data may simply be collected without user intervention. For example, data are collected as the vehicle travels over a region and/or while in motion. Stops by the driver may be made for other reasons (e.g., stop signs, traffic signals, etc.). Thus, the data may be passively captured during routine vehicle operation by individuals, as fleet vehicles or as part of a network of vehicles (e.g., vehicles that are distributed and may be independently as opposed to centrally operated, but which may be in communication with each other or with a central system).

402 402 402 402 200 2 2 In some embodiments, the route taken by one or more vehicles at stepis selected based on various criteria. For example, a route may be selected to evenly cover an area or to focus on regions at which VOCs are more likely to occur. The route may be updated during operation of the vehicle. For example, in response to regions in which the VOCs detected is high, part of the route may be re-traversed. In some embodiments, there may be no route specified for the purposes of data collection. Instead, data is collected as the vehicle travels through the routes selected by the operator. For example, the sensor system may be mounted in a private individual's vehicle and data collected as the private individual travels in the vehicle to carry out their own tasks. In addition to total VOCs (e.g., VOCs including but not limited to BTEXs), other data may be collected as part of step. For example, the time each sample is taken (i.e., a time stamp) and the location of each sample can be measured and recorded. Further, data for other environmental constituents (e.g., methane, ethane, NO, NO, PM2.5, black carbon, ozone, CO, and/or CO) may be similarly captured. In some embodiments, stepincludes sending the sensor data from the mobile sensor platform to a centralized or other data processing system. In some embodiments, stepimplements some or all of method.

404 At, VOC peaks are identified in the VOC sensor data.

406 At, non-VOC peaks are identified in the non-VOC sensor data.

408 At, the VOC peaks are correlated with the non-VOC peaks.

404 406 408 404 406 408 402 102 150 402 The data are processed at steps,and. In some embodiments, steps,andare performed asynchronously with. For example, one or more mobile sensor platforms such as systemsA, may collect and transmit data to a centralized systemfor processing as part of.

404 406 408 404 After some or all of the data is received at the processing system, steps,andare performed. VOC peaks (if any) are identified from the VOC sensor data, at step. In various embodiments, any event detection methods via signal processing could be used to detect VOC peaks among VOC sensor data. For example, identification of peaks includes determining whether the amount of the corresponding pollutant exceeds a threshold for that pollutant. In various embodiments, when the received mobile sensor data includes a time series of pollutant concentration, then a peak is a portion (e.g., a subset of data points/samples) within the time series in which the pollutant concentration exceeds a threshold. In some embodiments, the threshold for the pollutant is based on a rolling baseline. The rolling baseline is the median of the measurements of the pollutant for a given time window/temporal neighborhood (e.g., thirty seconds, a minute, two minutes, or four minutes) around the time the sample(s) were captured. In some embodiments, the threshold may be selected to be at least the baseline. Lower thresholds generally have a higher sensitivity to the pollutant being detected and may result in more peaks being identified. In some embodiments, the threshold may be selected based upon the noise in the sensor(s) that sample VOCs. For example, in various embodiments, sensor data has a threshold of at least two multiplied by the sensor noise floor, three multiplied by the sensor noise floor, four multiplied by the sensor noise floor, five multiplied by the sensor noise floor, six multiplied by the sensor noise floor, ten multiplied by the sensor noise threshold, twelve multiplied by the sensor noise floor, or a greater multiple of the sensor noise floor. Data exceeding the corresponding threshold may be counted as an accurate measurement of a significant enhancement in VOCs.

The identified VOC peaks identified are associated with a location. For example, if a VOC peak were associated with multiple data points in the sensor data stream, then the location associated with the VOC peak could be the center of the recorded location data associated with those data points. The location associated with a VOC peak can be any spatial unit such as, for example, a particular road segment, particular latitude/longitude coordinates, and/or a particular grid cell (e.g., a hexbin (a hexagonal grid cell), or other shape of which the road segments on which data are accrued are part of). The VOC peaks may thus be considered to be identified as peaks, geolocated (e.g., to a road segment, to latitude/longitude coordinates, to a hexbin, or to another location) and timestamped. The timestamp corresponds to the time at which the data is collected. For example, if a VOC peak were associated with multiple data points in the sensor data stream, then the location associated with the VOC peak could be the average of the recorded timestamps associated with those data points.

406 406 Non-VOC peaks can be determined from non-VOC sensor data in stepanalogously to how VOC peaks are determined in step. For example, each non-VOC peak can be determined as a portion of a time series of non-VOC pollutant data points with pollutant concentration that exceeds a threshold and where the threshold is determined as a function of a rolling baseline.

408 408 404 406 It is also determined whether the VOC peaks and the non-VOC peaks are correlated, at step. Peaks being correlated include at least one of a correlation in peak shape, peaks being temporally co-located (e.g., having data time stamped within a particular interval), peaks being geographically co-located (e.g., the locations being correlated as occurring within a particular distance), and/or peaks having the appropriate ratio for a particular type of constituents. For example, the particular interval over which peaks can be determined to be temporally co-located can be peak dependent. For instance, imagine a plume through which a vehicle with a mounted mobile sensor system is driving. Other peaks could be identified within the plume as correlated, and then the sensors' time within the plume via the peak's shape is determined. Then, correlation can be expressed as a time interval based on that time-in-plume. For example, the locations of these peaks may be correlated (e.g., considered to be at the same location/be co-located) if determined to be within a threshold distance, such as, for example, less than thirty meters, of each other. In some embodiments, the locations of the peaks may be determined and correlated to within ten meters. In some embodiments, the locations of the peaks may be determined and correlated to within six meters. Temporal correlations may be determined in an analogous manner. For example, the time of the peaks may be determined and correlated to within 24 hours. Thus, there may be a difference in the time that VOC peaks and/or other components of the atmosphere (e.g., non-VOC peaks) are detected even if they are from the same source. Thus, data for VOC peaks and/or other environmental components (e.g., non-VOC peaks) may be time shifted as part of correlating the peaks in stepand/or determining the existence of peaks at stepsand. This time shifting may relate to the instrument response time and to the residence time of the air sample in a manifold that routes air samples to the sensors.

408 408 As part of the correlation determined at step, statistics on the VOC peaks and correlated non-VOC peaks from other sensed components of the atmosphere are collected. This data may be used in conjunction with other data that is collected. For example, the peak-to-pass ratio (e.g., the number of times a peak is detected at a location divided by the number of times data is collected by a mobile sensor platform for the location while driving a distinct route), the number of distinct dates for which a peak is found at a location, and/or whether there are three (or some other number greater than one) separate indicators of the peak being present within twenty-five meters (or some other distance) of the location may be determined using the analyzed VOC data. Of these, the separate indicators of the peak may be determined atfrom a dataset for a single route.

410 At, a source type associated with the VOC peaks are determined based at least in part on the correlation between the VOC peaks and the non-VOC peaks.

408 2 The source type of each VOC peak is determined based on that VOC peak's correlation (or lack thereof) with non-VOC peaks at step. In various embodiments, the “source type” of a VOC peak is a classification/category of the type of source from which the VOCs in the VOC peak may have originated. In various embodiments, any number of source types may be configured. Some specific examples of source types include a combustion source type (where VOCs were emitted from a source of a combustive/burning event), a non-combustion VOC source type (where VOCs were emitted from a non-combustive/burning event), and a third VOC (e.g., only) source type (where VOCs were emitted from a source that is neither combustive or non-combustive but evaporative). For example, if no non-VOC peak is co-located with a VOC peak (i.e., the peak(s) for a location or region are VOC only), the source type of the VOC peak may be VOC only (which is sometimes herein referred as a “TVOC” source type). Examples of a VOC only (TVOC) source type may include an evaporative source such as a gas station, leaking fuel tank, or nail salon. If CO, CO, and/or NO types of combustive but non-VOC peaks are co-located with the VOC peak, the source type may be a combustion source type (which is sometimes herein referred as a “combustion” source type). Examples of a combustion source type may include moving traffic or another location that burns fuel. If non-combustive types of non-VOC peaks are co-located with the VOC peak, the source type may be a non-combustion source type (which is sometimes herein referred to as a “TVOC+” source type). An example of a TVOC+ source type may include cooking emissions from a restaurant. The locations of VOC peaks of the combustion, non-combustion, and other source types may be in proximity to the actual locations from which they were emitted. For example, the VOC peaks associated with combustion sources (which are primarily from on-road mobile emissions) may be ignored if only stationary sources of VOCs are of primary interest. A VOC peak's source type classification may be determined based on correlations (or not) with non-VOC peaks, as described herein, and/or using decision tree(s) and probabilistic models.

102 402 102 102 102 102 150 404 406 408 410 102 150 402 2 For example, one or more of mobile sensor systemsare used to passively sample the environment in a moving vehicle at step. Sensor systemsthus capture total VOC sensor data over the route(s) driven. Other data, such as NO, CO, CO, PM2.5, methane, ethane, and/or other constituents of the environment may also be captured by the sensor systemsover the same route(s) by the same sensor systems. The data collected by system(s)may be uploaded to serverfor processing at steps,,and. This upload may take place remotely (e.g., while the vehicle carrying sensor systemis driven) or while the vehicle is in proximity to server. VOC and other non-VOC sensor data are processed. Thus, for each route driven (e.g., each series of time stamped data collected), the VOCs are detected and corresponding peaks are identified. Similar processing may take place for other non-VOC measurements. VOC and other peaks may be correlated for the route. Based on the data captured, as well as other information such as wind speed; maps indicating the locations of gas stations and/or other VOC sources; and topographical maps, the location of the source may also be determined for the data sensed at.

400 102 102 102 150 102 400 400 400 400 Using method, VOC peaks may be detected and ultimately classified into a source type. The method may be extended to be used with a number of systems, such as systemsA,B andC and/or server. Peaks from different sensor systemsmounted on different vehicles may be used to detect VOC peaks using method. Methodmay also be repeated over time internals using one or more systems. Thus, the VOCs, peaks in VOCs, emission source type (e.g., combustion, non-combustion, and/or other), and/or possible sources may be determined. Methodmay be repeated multiple times using the same or different mobile sensing systems simultaneously and at different times. Repeating methodover the course of a specified time period (e.g., over a month, a quarter, a year, and/or multiple years) may provide temporal information about when, where, and how many VOC peaks are detected over the specified time period.

5 FIG. 1 FIG. 4 FIG. 500 500 100 500 102 102 102 150 500 402 404 406 400 500 depicts an exemplary embodiment of methodfor the detection of peaks within sensor data streams. Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel). In some embodiments, steps,, andof processofmay be implemented, at least in part, using process.

502 At, a plurality of VOC sensor data streams is received from a plurality of VOC mobile sensors, wherein the plurality of VOC sensor data streams is associated with time and location information. In some embodiments, the VOC (e.g., TVOC) sensor data streams were received from VOC (e.g., TVOC) sensors that were part of mobile sensor platforms. In some embodiments, each VOC sensor data stream is a time series of VOC measurements/data points and where each data point (e.g., measured TVOC concentration) is associated with a corresponding location (e.g., GPS coordinate) and timestamp.

504 At, a set of VOC peaks is determined by determining peaks within the plurality of VOC sensor data streams. In various embodiments, VOC peaks can be determined from VOC sensor data streams using any event detection methods via signal processing. In some embodiments, a sliding window is applied to each VOC data (e.g., measured concentration) time series to determine whether a VOC peak is present within that window. In some embodiments, a VOC peak is determined within a sliding window (e.g., that is thirty seconds, a minute, two minutes, or four minutes in width) if the measured VOC sensor data exceeds a threshold and where, for example, the threshold is determined as a function (e.g., multiple) of a predetermined value and the rolling VOC sensor baseline within the sliding window. The rolling baseline is the median of the measurements of the VOC sensor data within the sliding window. Put another way, a VOC peak is determined as a peak in measured VOC (e.g., TVOC) concentration within a temporal neighborhood. A VOC peak is associated with a corresponding location (e.g., which is determined as the center of the location information associated with the VOC data point(s) that are included in the VOC peak) and a corresponding time (e.g., which is determined as the average of the timestamps associated with the VOC data point(s) that are included in the VOC peak.

506 2 2 At, a plurality of non-VOC sensor data streams is received from a plurality of non-VOC mobile sensors, wherein the plurality of non-VOC sensor data streams is associated with time and location information. In some embodiments, the non-VOC sensor data streams were received from different non-VOC types of sensors (e.g., methane, ethane, NO, NO, PM2.5, black carbon, ozone, CO, and/or CO) that were part of mobile sensor platforms. In some embodiments, each non-VOC sensor data stream is a time series of non-VOC measurements/data points and where each data point (e.g., measured TVOC concentration) is associated with a corresponding location (e.g., GPS coordinate) and timestamp.

508 504 At, a set of non-VOC peaks is determined by determining peaks within the plurality of non-VOC sensor data streams. In some embodiments, non-VOC peaks can be determined analogously to how VOC peaks are determined at step.

6 FIG. 6 FIG. 6 FIG. 6 FIG. 6 FIG. 602 602 610 612 602 610 604 604 606 608 604 604 606 608 604 604 606 608 602 612 604 604 606 608 2 6 4 2 is a diagram that shows respective data streams for VOC sensor data and various non-VOC sensor data. In particular,shows the detailed temporally and spatially resolved concentrations of VOCs and other non-VOC pollutants. Time seriesshows the measured data points (concentrations) of VOC (e.g., TVOC) across time. The same measured data VOC data points of time seriesare also shown at their corresponding recorded locations along a segment of a road as shown in birds-eye view of the road in image. VOC peak, which is associated with a high VOC concentration within its temporal neighborhood, is also detected within time seriesand its corresponding location along the road shown in imageis also identified.also shows time series,,, and(among others) that respectively show the measured data points (concentrations) of non-VOC pollutants such as black carbon, CN, CH, CO, and COacross time. Non-VOC peaks can be also similarly identified across each of time series,,, and. As shown in, time series,,, andof measured non-VOC data points are time-aligned and space-aligned with time seriesof measured VOC data points. In the specific of, VOC peakdoes not correlate either temporally or spatially with any on non-VOC peaks within time series such as,,, andand is therefore determined to be a source type associated with VOC only (which is sometimes herein referred to as “TVOC”).

7 FIG. 1 FIG. 4 FIG. 700 700 100 700 102 102 102 150 700 408 410 400 700 depicts an exemplary embodiment of methodfor the determining of a source type for a VOC peak based on the VOC peak's correlation(s) with other non-VOC peak(s). Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel). In some embodiments, stepsandof processofmay be implemented, at least in part, using process.

702 500 408 5 FIG. At, a (next) VOC peak is received. As described above, VOC peaks are detected among mobile sensor data (e.g., using a process such as processof). Each VOC peak is associated with time and location information. As described above, the VOC peak is to be correlated with non-VOC peaks along one or more attributes of: time, location/space, peak shape, and peak-to-pass ratio. For example, the locations of a VOC peak and a non-VOC peak may be correlated (e.g., considered to be at the same location/be co-located) if determined to be within a threshold distance, such as, for example, less than thirty meters, of each other. In some embodiments, the locations of the peaks may be determined and correlated to within ten meters. In some embodiments, the locations of a VOC peak and a non-VOC peak may be determined and correlated to within six meters. Temporal correlations may be determined in an analogous manner. For example, the time of the peaks may be determined and correlated to within 24 hours. Thus, there may be a difference in the time that a VOC peak and a non-VOC peak are detected even if they are from the same source. Thus, data for a VOC peak and a non-VOC peak) may be time shifted as part of correlating the peaks in stepand/or determining the existence of peaks. This time shifting may relate to the instrument response time and to the residence time of the air sample in a manifold that routes air samples to the sensors.

704 706 708 704 2 At, whether the VOC peak is correlated with combustive pollutant type peak(s) is determined. In the event that the VOC peak is correlated with combustive pollutant type peak(s), control is transferred to. Otherwise, in the event that the VOC peak is not correlated with combustive non-VOC pollutant type peak(s), control is transferred to. In particular, at step, the VOC peak in question is compared with non-VOC peaks associated with pollutants that are associated with combustions. Examples of such combustive pollutants include CO, CO, and NO.

706 2 At, it is determined that the VOC peak is associated with a combustion source type. If the VOC peak is correlated (e.g., within a given margin/threshold) along one or more attributes with any one or more combustive non-VOC pollutant (e.g., CO, CO, and/or NO) peaks, then the VOC peak is classified to be of a combustion source type (e.g., this source type is sometimes referred to as the “Combustion” source type), meaning that the VOC peak is potentially emitted from a source that is of a combustion (e.g., burning) nature (e.g., from car engines).

708 710 712 708 4 At, whether the VOC peak is correlated with non-combustive pollutant type peak(s) is determined. In the event that the VOC peak is correlated with non-combustive pollutant type peak(s), control is transferred to. Otherwise, in the event that the VOC peak is not correlated with non-combustive non-VOC pollutant type peak(s), control is transferred to. At step, the VOC peak in question is compared with non-VOC peaks associated with pollutants that are not associated with combustions. Examples of such non-combustive pollutants include methane (CH) and fine particulate matter (PM2.5).

710 At, it is determined that the VOC peak is associated with a non-combustive source type. If the VOC peak is correlated (e.g., within a given margin/threshold) along one or more attributes with any one or more non-combustive non-VOC pollutant peaks, then the VOC peak is classified to be of a non-combustive source type that is nevertheless still associated with other pollutants (e.g., this source type is sometimes referred to as the “TVOC+” source type), meaning that the VOC peak is potentially emitted from a source that is not related to a burning nature. An example of a TVOC+ source is a cooking process/restaurant.

712 At, it is determined that the VOC peak is associated with a VOC only source type. If the VOC peak is not correlated (e.g., within a given margin/threshold) along one or more attributes with any one or more other non-VOC peaks associated with either combustive or non-combustive pollutants, then the VOC peak is classified to be of a VOC-only source type (e.g., this source type is sometimes referred to as the “TVOC” source type), meaning that the VOC peak is potentially emitted from not a combustive but rather to an evaporative nature (e.g., a business the provides dry cleaning or nail painting services). Examples of TVOC source types includes a fuel leak, a nail salon, or a gas station.

714 702 700 At, whether there is at least one more VOC peak for which a source type is to be determined. In the event there is at least one more VOC peak for which a source type is to be determined, control is returned to. Otherwise, there are no more VOC peaks for which a source type is to be determined, processends.

8 FIG. is a diagram that indicates the location of the VOC (e.g., total VOCs) peaks detected in a first map region. Each circle represents a detected VOC peak. The location of each circle on the map is associated with the location of the represented VOC peak. The color/shade associated with each circle indicates the source type (e.g., TVOC, TVOC+, and Combustion) that to which the VOC peak has been classified. The size of each circle is indicative of the peak height (or strength) of the corresponding VOC peak.

9 9 FIGS.A-C 9 FIG.A 9 FIG.B 9 FIG.A 9 FIG.B 9 FIG.B 9 FIG.C 9 FIG.A 9 FIG.C 9 FIG.C 8 9 9 FIGS.andA-C depict various views for VOC data captured and analyzed in a second map region.is a diagram that indicates the location of the VOC (e.g., total VOCs) peaks detected on various road segments in the second map region. Each circle represents a detected VOC peak. The location of each circle on the map is associated with the location of the represented VOC peak. The color/shade associated with each circle indicates the source type (e.g., TVOC, TVOC+, and Combustion) that to which the VOC peak has been classified. The size of each circle is indicative of the peak height (or strength) of the corresponding VOC peak.is a closer view of a portion of the second region depicted in. In, the VOC peak locations are associated with road segments. Each road segment ofis labeled with a color/shade that matches the number of days on which at least one VOC peak has been detected on that segment within a given window of time.is another closer view of a portion of the second region depicted in. In, the VOC peak locations are associated with road segments. Each road segment ofis labeled with a color/shade that matches the peak to pass ratio of VOC peaks that have been detected on that segment (e.g., within a given window of time). Thus, as shown in, the collected VOC mobile sensor data may be associated with spatial/location-based presentations and therefore be more readily viewed and analyzed. As will be described below, in some embodiments, the individual VOC peaks may be clustered/aggregated into different spatial scales/spatial units (e.g., a hexagon-shaped grid cell or other shape of which the road segments on which data are accrued are part of) for further analysis and visualization.

10 FIG. 1 FIG. 1000 1000 100 1000 102 102 102 150 1000 depicts an exemplary embodiment of methodfor the clustering of VOC peaks according to spatial units to which the VOC peaks belong. Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel).

1002 At, VOC peaks are mapped to corresponding spatial units. In various embodiments, spatial units are defined across a region. Examples of spatial units include hexagon-shaped cells (e.g., hexbins), road segments, rectangles, or any other shape. In some embodiments, all spatial units across a region can share the same dimensions. For example, each hexagon-shaped cell can be 100 meters to 200 meters across. In some embodiments, spatial units across a region can have variable dimensions. As described above, each VOC peak is associated with metadata such as location information. As such, each VOC peak can be mapped to the spatial unit in which the VOC peak's location is located.

1004 500 1002 5 FIG. At, source type-specific clusters of VOC peaks are determined based on the spatial units that correspond to the VOC peaks. As described above, the VOC peaks have also been classified into source types (e.g., using a process such as processof). The source type classified VOC peaks are clustered based on the spatial units to which they have been mapped at step. In some embodiments, all VOC peaks that have been mapped to the same spatial unit and share the same source type classification are part of the same cluster. In some embodiments, a source type-specific VOC peak cluster in one spatial unit may be merged with a VOC peak cluster of the same source type in one or more neighboring spatial units.

In some embodiments, the weighted centroid of the VOC peaks in a cluster is defined as the cluster location. The weight for each location is the enhancement (e.g., magnitude) of the peak at that location. Thus, the location of the cluster is more influenced by the largest peak in the cluster. Once VOC peaks are grouped into clusters, an area (e.g., a set of neighboring spatial units) encompassed by the cluster may also be defined. This may be accomplished by buffering the individual spatial units that include a portion of a cluster and combining these buffers into a single polygon. In some embodiments, this is parameterized with a buffer distance. In addition, other methods could be used to define the cluster area. For example, a bounding box or convex hull may be utilized, but may generally overestimate the cluster area.

1006 At, for each spatial unit, statistical information associated with at least a subset of the VOC peaks that have been clustered in that spatial unit is determined. Statistical information associated with all the VOC peaks that have been mapped to each spatial unit and the source type specific VOC peak clusters that are located within the spatial unit are determined. Examples of such per-spatial unit statistical information include the number of (any source type) VOC peaks that are located within the spatial unit and the respective percentage of each source type VOC peak (e.g., there is 70% combustion VOC peaks, 20% TVOC+VOC peaks, and 10% TVOC peaks) that have been clustered within the spatial unit.

12 FIG. In some embodiments, in addition to determining per-spatial unit statistics, statistics/metrics are also calculated for each cluster, as will be described in.

In some embodiments, a presentation of the spatial units across a region can be generated and where each spatial unit is represented to indicate at least one statistical attribute associated with the spatial unit.

1008 1010 1000 At, whether reclustering of the VOC peaks should be performed is determined. In the event that reclustering of the VOC peaks should be performed, control is transferred to. Otherwise, in the event that reclustering of the VOC peaks should not be performed, processends. The spatial unit statistics and/or the cluster statistics as described above, and the metrics regarding the number of spatial units/clusters that include VOC peaks can be compared to reclustering criteria to determine whether the VOC peaks should be reclustered across spatial units with updated sizes. For example, the reclustering criteria can determine whether the VOC peaks have been sufficiently spread across a desired distribution of spatial units to avoid an undesirable low (e.g., one) number of VOC peak cluster for each source type or avoid an undesirable high number of VOC peak cluster for each source type. Depending on whether the reclustering criteria is met, the spatial unit may be sized up or down. For example, if there are too few clusters that were formed in the previous clustering process, then the size of the spatial units can be decreased so that the VOC peaks would map across more spatial units and result in more clusters. Also, for example, if there are too many clusters that were formed in the previous clustering process, then the size of the spatial units can be increased so that the VOC peaks would map across fewer spatial units and result in fewer clusters.

1010 1008 1002 At, the size of a spatial unit is updated. The size of the spatial units are adjusted according to the reclustering criteria that was met atand the clustering process restarted at step.

11 FIG. 11 FIG. 11 FIG. 1102 1104 is a diagram showing at least some spatial units across a particular region. In the example of, the region has been divided into spatial units that are hexagons. Each hexagon in the example ofis shown with a shade that represents the count of VOC peaks (of any source type) that are located within that hexagon and where the darker the shade, the more VOC peaks that are located within that hexagon. As such, hexagonis shown to include more VOC peaks than hexagon.

11 FIG. While not shown in, the VOC peaks that are located within each hexagon can be separated into clusters based on the source types to which they have been classified. Also, statistical information related to the VOC peaks and/or their respective source type classifications can be determined on a per hexagon basis and, optionally, output at a user interface.

In some other embodiments, a clustering mechanism such as DBSCAN may be used. Thus, the clustering may be based on the minimum number of points in a VOC peak, the maximum distance between two VOC peaks for the peaks to be considered part of the same cluster, and the distance metric which is a measure of how the distances between samples are calculated. In some embodiments, the minimum number of points selected may be as small as three. In other embodiments, other minimum numbers of points might be used. The distance is the maximum distance between two VOC peaks for them to be considered part of the same cluster. In some embodiments, the distance is twenty-five meters. However, in other embodiments, other distances may be used. In some embodiments, the Haversine distance is used as the distance metric. Other measures may be used in other embodiments.

12 FIG. 1 FIG. 1200 1200 100 1200 102 102 102 150 1200 depicts an exemplary embodiment of methodfor determining statistical attributes related to source type-specific clusters of VOC peaks. Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel).

1202 1000 10 FIG. At, statistical attributes corresponding to a plurality of source type-specific clusters of VOC peaks. As described above in processof, clusters of VOC peaks of respective source type classifications (e.g., TVOC, TVOC+, and combustion) are determined based on the spatial/location information of each VOC peak. As a result of such clustering, zero or more clusters of VOC peaks at proximate locations to each other (e.g., are located within the same or neighboring spatial units) and of the same VOC peak source type (e.g., TVOC, TVOC+, and combustion) are included in the same cluster. Statistical attributes are determined for each such cluster of source type-specific VOC peaks of a respective source type classification (e.g., TVOC, TVOC+, and combustion). Examples of statistical attributes that can be determined with respect to each source type-specific cluster include one or more of the following: the average peak height/strength of the VOC peaks of the cluster, the count of distinct days on which the VOC peaks in the cluster were detected, the average frequency of occurrence of VOC peaks of the cluster, sampling details, peak summaries, and source summaries. Sampling details provide descriptions of the cluster itself and information related to sampling effort. Peak summaries provide summary metrics of a few selected peak fields. Source summaries provide a summary of metrics related to the calculated/determined leak rate and distance estimated from the data. Additional and/or other metrics may be determined in other embodiments. In some embodiments, for example, the sampling details may include the peak count, the number of passes, the detection rate, the number of days having a peak, the number of days driven, the average enhancement for each peak, the enhancement standard of deviation, the average maximum values, the maximum standard of deviation, the time or number of passes since anything within the cluster has been detected, and the number of peaks within a cluster detected within a defined number of passes. For the peak count, the peak count includes the number of VOC peaks included in the cluster. A concurrent peak count may include the number of peaks where the VOC peak and another peak are concurrent (e.g., occurring at substantially the same time and location and, in some embodiments, which are correlated as described above). The number of passes is the number of times the vehicle(s) having mobile sensor platform(s) was/were driven through the cluster. Other and/or additional statistics may be determined in other embodiments.

1204 At, the plurality of source type-specific clusters of VOC peaks is ranked based on the statistical attributes. In some embodiments, clusters of the same source type can be ranked among themselves so that a respective ranked list of clusters is determined for each source type. For example, each source type-specific cluster can be scored as a function of its determined statistical attributes and where a higher score corresponds to a higher priority that is needed to address/further investigate the cluster (e.g., a more persistent location/source of VOC-related pollutants).

1206 At, a presentation that includes representations of the plurality of source type-specific clusters of VOC peaks is optionally output at a user interface. Optionally, representations of the source type-specific clusters of VOC peaks can be output at a user interface. For example, the presentation can include representations of the source type-specific clusters of VOC peaks on a map. In another example, the presentation can include ranked lists of clusters of VOC peaks according to their source type classifications.

1208 1210 1200 At, whether a selection of a cluster is selected is determined. In the event that a selection of a cluster is selected, control is transferred to. Otherwise, in the event that a selection of a cluster is not selected, processends. For example, a user selection of a particular source type-specific cluster can be made in the user interface displaying representations of the source type-specific clusters of VOC peaks on a map.

1210 1202 At, detailed information on VOC peaks included in the selected cluster is presented. In response to a selection of the source type-specific cluster, additional information related to the cluster can be presented at the user interface. Examples of such additional information may include the statistical attributes that were determined at step, a presentation that is determined based on the timestamps associated with the VOC peaks included in the cluster, and representations of a map of each individual VOC peak that is included in the cluster.

1212 1202 1200 1202 At, whether updated clusters are to be determined. In the event that updated clusters are to be determined, control is returned to. Otherwise, in the event that no more updated clusters are to be determined, processends. Stepcan be returned in the event that updated clusters of source type-specific VOC peaks are determined.

13 FIG. 1 FIG. 1300 1300 100 1300 102 102 102 150 1300 depicts an exemplary embodiment of methodfor determining source locations corresponding to source type-specific clusters of VOC peaks. Methodis described in the context of systemof, but may be performed using other systems. Specifically, as described below, methodmay be carried out on multiple sensor systems, such as mobile sensor systemsA,B andC and server. For clarity, only some portions of methodare shown. Although shown in a sequence, in some embodiments, processes may occur in parallel and/or in a different order (including in parallel).

1300 Processdescribes an example flow of inferring locations of sources that resulted in the VOC peaks of a cluster based on the cluster's correlation with third-party data (e.g., meteorological data). In other examples, locations of sources that resulted in the VOC peaks of a cluster can be determined based on other techniques.

1302 102 At, a plurality of source type-specific cluster of VOC peaks is correlated with third-party data. Examples of third-party data includes meteorological data. For example, the wind conditions during the period over which the VOC mobile sensor data was collected may be determined. In some embodiments, third-party meteorological data (e.g., prevailing winds) may be correlated with the VOC peaks of each cluster. This may aid in determining the direction to the source of VOC peaks (e.g., upwind) that belong to the same cluster. For example, in some embodiments, the wind direction may be located to within a quadrant (e.g., to within 90 degrees of a particular direction). In some embodiments, wind data may be sensed by the sensor system (e.g., mobile sensor systems) during its regular sensor data collection operations. Other data (e.g., temperatures, barometric pressure, precipitation, etc.) may also be correlated with each cluster's VOC peak data. Other examples of third-party data with which the VOC peaks can be correlated include sensor data from third-party fleets of mobile sensor platforms and/or data from remote sensing (e.g., satellite data).

1304 At, source locations corresponding to the plurality of source type-specific clusters of VOC peaks is determined based on the correlations with the third-party data. The clusters' correlations with third-party data such as, for example, meteorological data may be analyzed to detect, locate, and characterize the source locations of the VOC peaks associated with each source type-specific cluster. For example, identification of the source locations (e.g., combustion, non-combustion, or something else related), geographic proximity of the peaks to the source locations, wind direction and speed used to determine the source locations, vehicle direction and speed, peak intensity and other peak characteristics may be analyzed to determine for each source type-specific cluster, one or more potential source locations. For example, the VOC peaks' of a cluster's correlations with wind conditions may indicate that the VOC peaks are consistently downwind of a particular source location that has characteristics that match the cluster's source type (e.g., combustion). In some embodiments, source locations are determined from a predetermined list of businesses or a list of points of interest.

1306 At, the source locations is ranked based on statistical attributes corresponding to the plurality of source type-specific clusters of VOC peaks. Possible source locations corresponding to each cluster may be prioritized and ranked based on their priority. The priority of a source location represents, for example, the extent to which VOC peaks are persistent, strong, recent, and/or further attention at the source location is warranted. The priority of a source location may be determined, for example, by one or more of the following: the intensity of the VOC peaks, the number of VOC peaks in a given region, the peak-to-pass ratio (number of times a peak is detected divided by the total number of times a location was passed), the number of distinct days the peak is detected (or other measure of the frequency of detection), the persistence of detection (e.g., the number of consecutive distinct days the peak is detected), the (e.g., historical) emission rate of the source location, and whether the source location corresponds to a correctable emission source (e.g., a natural gas leak).

1308 1310 1310 At, whether further action is recommended is determined. In the event that the further action is recommended, control is transferred to. Otherwise, in the event that the further action is not recommended, control is transferred to. In some embodiments, further action is recommended on at least a portion of the source locations that have been determined for the source type-specific clusters of VOC peaks. For example, the source location(s) with priorities over a threshold priority are recommended for further action. In another example, the source location(s) corresponding to a predetermined number of highest priorities are recommended for further action.

1310 102 At, further action corresponding to a source location associated with a specified source type-specific cluster is prompted. To prompt for further action on a source location includes to send an alert to a client and/or present at a user interface a prompt that identifies the source location and the source type-specific cluster to which it corresponds. For example, further action may include deploying equipment with higher sensitivity and/or pollutant speciation ability to the region (e.g., the source location and/or location of the cluster) of persistent peaks. The speciation of pollutants (e.g., by canister sampling, gas chromatography, mass spectrometry, optical spectroscopy, etc.) provided by the deployment of such equipment for targeted surface may be utilized to further detect specific VOC pollutants (which is sometimes refer to as making speciated VOC measurements) and use that information to confirm the source location/reason of the VOCs. In some embodiments, the correlation with third-party (e.g., meteorological) data in combination with the speciation may be used to target and identify the specific source of VOC(s). Such higher sensitivity equipment may be significantly more expensive than mobile sensing systemsand may take longer to take individual measurements. Consequently, the identification of persistent VOC peaks and the correlation with other data (e.g., other pollutants and/or meteorological data) may provide an important trigger for such targeted surveys. This additional data provided by targeted surveys may also be used to draw additional conclusions such as whether emitters of VOCs are in compliance with the parameters of their permits, whether the emitters of VOCs are unpermitted, and/or whether mitigation actions are desired.

1312 1302 1300 1302 At, whether updated clusters are to be determined. In the event that updated clusters are to be determined, control is returned to. Otherwise, in the event that no more updated clusters are to be determined, processends. Stepcan be returned in the event that updated clusters of source type-specific VOC peaks are determined.

1300 Thus, as shown above in process, the source locations corresponding to clusters of VOC peaks may be characterized over time. For example, the priority of the source location may be adjusted based upon changes to its characteristics, detection of new source locations and/or changes to other source locations.

1300 1300 Using process, multiple sensor systems mounted on multiple vehicles and/or multiple passes over a region by the same vehicle may enhance detection of VOCs. Processmay also be repeated over time internals using one or more systems. Thus, changes in the presence of VOC peaks may be determined. For example, the effectiveness of regulatory schemes and/or punishments may be determined. Thus, mitigation of the effects of VOCs and other emissions may be improved. Further, the cost of sources such as VOC leaks may also be reduced. These benefits may be achieved while passively and more efficiently collecting environmental data.

14 FIG. 4 FIG. 5 FIG. 7 FIG. 10 FIG. 12 FIG. 13 FIG. 100 400 500 700 1000 1200 1300 is a diagram showing a satellite map depicting a closer view of VOC peaks (associated with the same cluster) detected on the same road segment on two distinct dates, indicating persistence of a VOC source over time in this location. The possible source location of the four shown VOC peaks may be identified as the fuel storage tanks in proximity to the peaks, as shown in the satellite map base layer. Thus, using systemand processes such as processof, processof, processof, processof, processof, and/or processof, the source locations of VOC peaks of a cluster may be tentatively identified. More sensitive and/or pollutant speciation equipment may be deployed to confirm whether VOCs are leaking from the fuel storage tanks, identify the specific VOC pollutant(s) leaking, and propose and undertake mitigation measures.

500 102 102 102 5 FIG. In various embodiments, VOC mobile sensor data and non-VOC mobile sensor data are continuously collected and analyzed for peaks (e.g., using a process such as processof). Data is collected on multiple sensor systemsmounted on multiple vehicles and/or by multiple passes over the same region by the same vehicle having a sensor system. For example, a fleet of vehicles, each of which includes one or more sensor systems, may be driven over routes and data passively collected. As previously indicated, the routes may be determined to map a particular area, to focus on a region at which a leak is predicted to occur, and/or for other purposes. In some embodiments, private individuals' vehicles may include sensor systems. Data collection occurs while the individuals use their vehicles in daily life. In some embodiments, a combination of a fleet of vehicles and private vehicles may be used. Data is collected passively on mobile platforms. For example, multiple vehicles may be used on the same day to collect data over a particular area. The data may also be collected (using the same or different routes) periodically. Thus, a time series of data to be captured for a region. For example, a particular location (e.g., a street segment or latitude/longitude defining the location) may be passed at least twice per month, and/or at least twenty times per year. Other total numbers of passes, frequencies of passes, and/or data collection periods may be used.

102 700 1200 102 150 400 1200 1300 7 FIG. 12 FIG. 4 FIG. 12 FIG. 13 FIG. The data from the multiple sensor platformson multiple vehicles is analyzed to find peaks in various components, correlate the peaks, and identify the source type of VOC peak (e.g., combustion/non-combustion, TVOC+particular other constituent(s), TVOC only) (e.g., using a process such as processof). Periodically or in response to a trigger, the current source type classified VOC peaks are clustered (e.g., using a process such as processof). In some embodiments, sensor platformsupload data to server, which processes the data. Locations, times, shapes, and other data for the peaks may be determined in a manner analogous to that discussed with respect to methodof. Further, the persistence of the peaks may also be determined as part of processofor processof. For example, the peak-to-pass ratio (the number of passes of a location for which a peak is identified divided by the total number of passes for the location), the number of distinct days for which a peak is identified at the location and/or other measures of the presence of the peak over time may be determined.

VOC detection as described herein provide analysis and correlation of VOC data (e.g., total VOCs). VOCs may be sensed at hyperlocal scales and analyzed. For example, total VOCs over a region may be rapidly sensed and tracked. As a result, potential VOC sources may be identified and tracked over time from the VOC data that is collected at scale, without any prior knowledge of candidate source locations of VOC emissions. Further, slower and more sensitive equipment may be more efficiently/selectively deployed (e.g., to areas with persistent VOC peaks). Thus, regulation, mitigation of leaks, and management of VOC emitters may be improved.

Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive.